The glass transition temperature (T-g) is one of the most important properties affecting the stability of a polymeric material. A cheminformatics-based approach has been employed to investigate the glass transition temperatures for a set of polymers. Specifically, a set of 80 polymers was used to build a quantitative structure-property relationship (QSAR). By applying a combination of cheminformatics methods, several predictive models were developed consisting of 1-10 physicochemical variables. The best predictive model, which is based on seven descriptors, successfully predicts the glass transition temperatures for the investigated polymers. Furthermore, the best developed model identified several significant descriptors responsible for glass transition temperatures of the investigated polymers with a correlation coefficient of r(2)=0.77. The computational model derived from this study may serve as a powerful tool to predict glass transition temperatures for various polymers. (C) 2018 Wiley Periodicals, Inc.